code: def small_mind_image_recognition():
      """!
      @brief Trains network using letters 'M', 'I', 'N', 'D' and recognize each of them with and without noise.
  
      """
      images = []
      images += IMAGE_SYMBOL_SAMPLES.LIST_IMAGES_SYMBOL_M
      images += IMAGE_SYMBOL_SAMPLES.LIST_IMAGES_SYMBOL_I
      images += IMAGE_SYMBOL_SAMPLES.LIST_IMAGES_SYMBOL_N
      images += IMAGE_SYMBOL_SAMPLES.LIST_IMAGES_SYMBOL_D
  
      template_recognition_image(images, 100, 10, 0.2)
  
file_path: pyclustering/pyclustering/nnet/examples/syncpr_examples.py
imports: from pyclustering.samples.definitions import IMAGE_SYMBOL_SAMPLES
  from pyclustering.utils import read_image, rgb2gray
  from pyclustering.nnet import solve_type
  from pyclustering.nnet.syncpr import syncpr, syncpr_visualizer
  import random
  import math
label_desc: Use image node_id 2 for calling a global function with example usage: small_mind_image_recognition() without return types